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Virtual healthcare services and digital health technologies deployed during coronavirus disease 2019(COVID-19)pandemic in South Africa:a systematic review

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Global Health Journal 2022年第2期6卷 102-113页

作者： Elliot Mbunge John Batani Goabaone Gaobotse Benhildah Muchemwa department of computer Science Faculty of Science and EngineeringUniversity of EswatiniKwaluseniManziniEswatini department of information Technology Faculty of Accounting and InformaticsDurban University of TechnologySouth Africa faculty of Engineering and Technology Botho UniversityLesotho department of Biological sciences and Biotechnology Faculty of ScienceBotswana International University of Science and TechnologyBotswana

Aims:To identify virtual healthcare services and digital health technologies deployed in South Africa during coronavirus disease 2019(COVID-19)and the challenges associated with their ***:To determine the status of digital health utilization during COVID-19 in South Africa,the preferred reporting items for systematic reviews and meta-analyses model was used to perform a systematic and in-depth critical analysis of previously published studies in well-known and trusted online electronic databases using specific search keywords words that are relevant to this *** selected published peer-reviewed articles available from the onset of COVID-19 to July ***:Total of 24 articles were included into this *** study revealed that South Africa adopted digital technologies such as SMS-based solutions,mobile health applications,telemedicine and telehealth,WhatsApp-based systems,artificial intelligence and chatbots and robotics to provide healthcare services during COVID-19 *** innovative technologies have been used for various purposes including screening infectious and non-infectious diseases,disease surveillance and monitoring,medication and treatment compliance,creating awareness and *** study also revealed that teleconsultation and e-prescription,telelaboratory and telepharmacy,teleeducation and teletraining,teledermatology,teleradiology,telecardiology,teleophthalmology,teleneurology,telerehabilitation,teleoncology and telepsychiatry are among virtual healthcare services delivered through digital health technologies during COVID-19 in South ***,these smart digital health technologies face several impediments such as infrastructural and technological barriers,organization and financial barriers,policy and regulatory barriers as well as cultural ***:Although COVID-19 has invigorated the use of digital health technologies,there are still some *** outbreak of pandemics like COVID-19 in t

关键词： digital health technology Virtual healthcare Coronavirus disease 2019(COVID-19) Health systems South Africa

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Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

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computers, Materials & Continua 2021年第8期68卷 2377-2397页

作者： Mueen Uddin M.S.Memon Irfana Memon Imtiaz Ali Jamshed Memon Maha Abdelhaq Raed Alsaqour faculty of Science Universiti Brunei DarusslamGadongBE1410Negara Brunei Darussalam CSE department QUESTNawabshahPakistan School of Computing and Mathematics Ulster UniversityJordanstown CampusNewtownabbeyBT370QBUK department of information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia department of information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia

Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients ***,the existing EHR systems mostly lack in providing appropriate security,entrusted access control and handling privacy and secrecy issues and challenges in current hospital ***:To solve this delicate problem,we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR ***:In our EHR blockchain system,Peer nodes from various organizations(stakeholders)create a ledger network,where channels are created to enable secure and private communication between different stakeholders on the ledger *** patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider(MSP)component of the fabric ***:We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the *** proposed fabric architecture provides a secure,transparent and immutable mechanism to store,share and exchange EHRs in a peer-to-peer network of different healthcare *** ensures interoperability,scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.

关键词： Electronic health records blockchain hyperledger fabric patient data privacy private permissioned blockchain healthcare ecosystem

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Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

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Intelligent Automation & Soft Computing 2023年第11期38卷 153-168页

作者： Ibrahim M.Alwayle Hala J.Alshahrani Saud S.Alotaibi Khaled M.Alalayah Amira Sayed A.Aziz Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Manar Ahmed Hamza department of computer Science College of Science and ArtsSharurahNajran UniversitySharurahSaudi Arabia department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of information Systems College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia department of digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt computer department Applied CollegeNajran UniversityNajran66462Saudi Arabia department of computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)*** presented AATS-HTDDNN technique aims to generate summaries of Arabic *** the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the *** study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization *** phase shows the novelty of the current *** validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was *** comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

关键词： Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language

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Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

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Intelligent Automation & Soft Computing 2023年第3期35卷 2773-2787页

作者： Amal H.Alharbi S.Karthick K.Venkatachalam Mohamed Abouhawwash Doaa Sami Khafaga department of computer sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia department of Electrical Electronics and Communication Engineering GITAM School of TechnologyGITAM Deemed to be UniversityBengaluru CampusKarnataka560065India department of computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end *** numerous security tech-niques,face recognition is considered a high-security *** fuse and carry out face identiﬁcation as an access authority into these ***,face identiﬁcation authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized *** the existing spooﬁng detection algorithm,there was some loss in the recreation of *** research proposes an unobtrusive technique to detect face spooﬁng attacks that apply a single frame of the sequenced set of frames to overcome the above-said *** research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused ***,this pro-posed method is tested with the Cross-ethnicity Face Anti-spooﬁng(CASIA),Fetal alcohol spectrum disorders(FASD)*** database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video *** images are taken with three different quality cameras:low,average,and high-quality real and spoofed *** extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-digital(DMAD)dataset that proved higher results when compared to existing algorithms.

关键词： Image processing edge detection edge net auto-encoder face authentication digital security

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Local Features-Based Watermarking for Image Security in Social media

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computers, Materials & Continua 2021年第12期69卷 3857-3870页

作者： Shady Y.El-mashad Amani M.Yassen Abdulwahab K.Alsammak Basem M.Elhalawany department of computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt department of Communications and Electronics Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt

The last decade shows an explosion of using social media,which raises several challenges related to the security of personal files including *** challenges include modifying,illegal copying,identity fraud,copyright protection and ownership of *** digital watermarking techniques embed digital information inside another digital information without affecting the visual quality for security *** this paper,we propose a hybrid digital watermarking and image processing approach to improve the image security ***,variants of the widely used Least-Significant Bit(LSB)watermarking technique are merged with a blob detection algorithm to embed information into the boundary pixels of the largest blob of a digital *** proposed algorithms are tested using several experiments and techniques,which are followed by uploading the watermarked images into a social media site to evaluate the probability of extracting the embedding *** results show that the proposed approaches outperform the traditional LSB algorithm in terms of time,evaluation criteria and the percentage of pixels that have changed.

关键词： digital watermarking LSB social media blob detection image security

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Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

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computers, Materials & Continua 2023年第3期74卷 5267-5281页

作者： Muhammad Javaid Iqbal Muhammad Waseem Iqbal Muhammad Anwar Muhammad Murad Khan Abd Jabar Nazimi Mohammad Nazir Ahmad department of information Technology The Superior UniversityLahorePakistan department of Software Engineering The Superior UniversityLahorePakistan department of information sciences Division of Science and TechnologyUniversity of EducationLahorePakistan department of computer Science Government College UniversityFaisalabadPakistan Deptment of Oral and Maxillofacial Surgery Faculty of DentistryUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangorMalaysia

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their *** brain tumour masses occur due to malignant *** tissues must die so that new tissues are allowed to be born and take their *** segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance *** finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and *** could not work for large volume images simultaneously,and many errors occurred due to overwhelming image *** main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning *** research study proposed an automatic model for tumor segmentation in MRI *** proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy *** the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated *** the third step,the proposed model uses state-of-the-art Medical Image Computing and computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour *** of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and *** proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively.

关键词： Brain tumour segmentation magnetic resonance images modalities dice coefficient low-grade glioma U-Net

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Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social media

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computers, Materials & Continua 2023年第5期75卷 4137-4154页

作者： Manar Ahmed Hamza Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Abdulbaset Gaddah Nasser Allheeib Suleiman Ali Alsaif Badriyya B.Al-onazi Heba Mohsen department of computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia department of computer sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia department of information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia department of computer Deanship of Preparatory Year and Supporting StudiesImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic *** number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a *** and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion *** the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web ***,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social *** presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social *** attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature *** irony detection and classification,the DBN model is exploited in this *** last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)*** experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark *** simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.

关键词： Computational linguistics natural language processing deep learning irony detection social media

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Wild Gibbon Optimization Algorithm

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computers, Materials & Continua 2024年第7期80卷 1203-1233页

作者： Jia Guo JinWang Ke Yan Qiankun Zuo Ruiheng Li Zhou He Dong Wang Yuji Sato Hubei Key Laboratory of digital Finance Innovation Hubei University of EconomicsWuhan430205China School of information Engineering Hubei University of EconomicsWuhan430205China China Construction Third Engineering Bureau Installation Engineering Co. Ltd.Wuhan430074China Hubei Internet Finance information Engineering Technology Research Center Hubei University of EconomicsWuhan430205China College of Informatics Huazhong Agricultural UniversityWuhan430070China faculty of computer and information sciences Hosei UniversityTokyo184-8584Japan

Complex optimization problems hold broad significance across numerous fields and ***,as the dimensionality of such problems increases,issues like the curse of dimensionality and local optima trapping also *** address these challenges,this paper proposes a novel Wild Gibbon Optimization Algorithm(WGOA)based on an analysis of wild gibbon population *** two strategies:community search and community *** community search strategy facilitates information exchange between two gibbon families,generating multiple candidate solutions to enhance algorithm ***,the community competition strategy reselects leaders for the population after each iteration,thus enhancing algorithm *** assess the algorithm’s performance,CEC2017 and CEC2022 are chosen as test *** the CEC2017 test suite,WGOA secures first place in 10 *** the CEC2022 benchmark functions,WGOA obtained the first rank in 5 *** ultimate experimental findings demonstrate that theWildGibbonOptimization Algorithm outperforms others in tested *** underscores the strong robustness and stability of the gibbonalgorithm in tackling complex single-objective optimization problems.

关键词： Complex optimization wild gibbon optimization algorithm community search community competition

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Deep Learning for Depression Detection Using Twitter Data

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Intelligent Automation & Soft Computing 2023年第5期36卷 1301-1313页

作者： Doaa Sami Khafaga Maheshwari Auvdaiappan KDeepa Mohamed Abouhawwash Faten Khalid Karim department of computer sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathurChennai603203India department of computer Science and Engineering M.Kumarasamy College of EngineeringKarur639113India department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA

Today social media became a communication line among people to share their happiness,sadness,and anger with their *** is necessary to know people’s emotions are very important to identify depressed people from their *** depression detection helps to save people’s lives and other dangerous mental *** are many intelligent algorithms for predicting depression with high accuracy,but they lack the deﬁnition of such *** machine learning methods help to identify depressed *** the accuracy of existing methods was not *** overcome this issue,the deep learning method is used in the proposed method for depression *** this paper,a novel Deep Learning Multi-Aspect Depression Detection with Hierarchical Atten-tion Network(MDHAN)is used for classifying the depression ***,the Twitter data was preprocessed by tokenization,punctuation mark removal,stop word removal,stemming,and *** Adaptive Particle and grey Wolf optimization methods are used for feature *** MDHAN classiﬁes the Twitter data and predicts the depressed and non-depressed ***,the proposed method is compared with existing methods such as Convolutional Neur-al Network(CNN),Support Vector Machine(SVM),Minimum Description Length(MDL),and *** suggested MDH-PWO architecture gains 99.86%accuracy,more signiﬁcant than frequency-based deep learning models,with a lower false-positive *** experimental result shows that the proposed method achieves better accuracy,precision,recall,and *** also mini-mizes the execution time.

关键词： Depression detection twitter data tweets deep learning swarm intelligence multi-aspect depression detection prediction

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A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

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computer Modeling in Engineering & sciences 2024年第7期140卷 1-40页

作者： Xianwei Jiang Yanqiong Zhang Juan Lei Yudong Zhang School of Mathematics and information Science Nanjing Normal University of Special EducationNanjing210038China School of Computing and Mathematical sciences University of LeicesterLeicesterLE17RHUK department of information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into *** article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 *** Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional *** from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung *** Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence *** addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification *** language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition ***,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among *** to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition ***,suitable datasets and evaluation criteria are also worth pursuing.

关键词： Chinese Sign Language Recognition deep neural networks artificial intelligence transfer learning hybrid network models

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